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- W3094062017 abstract "Incorrect drug target identification is a major obstacle in drug discovery. Only 15% of drugs advance from Phase II to approval, with ineffective targets accounting for over 50% of these failures1-3. Advances in data fusion and computational modeling have independently progressed towards addressing this issue. Here, we capitalize on both these approaches with Rosalind, a comprehensive gene prioritization method that combines heterogeneous knowledge graph construction with relational inference via tensor factorization to accurately predict disease-gene links. Rosalind demonstrates an increase in performance of 18%-50% over five comparable state-of-the-art algorithms. On historical data, Rosalind prospectively identifies 1 in 4 therapeutic relationships eventually proven true. Beyond efficacy, Rosalind is able to accurately predict clinical trial successes (75% recall at rank 200) and distinguish likely failures (74% recall at rank 200). Lastly, Rosalind predictions were experimentally tested in a patient-derived in-vitro assay for Rheumatoid arthritis (RA), which yielded 5 promising genes, one of which is unexplored in RA." @default.
- W3094062017 created "2020-10-29" @default.
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- W3094062017 date "2020-10-26" @default.
- W3094062017 modified "2023-10-15" @default.
- W3094062017 title "Preclinical validation of therapeutic targets predicted by tensor factorization on heterogeneous graphs" @default.
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- W3094062017 doi "https://doi.org/10.1038/s41598-020-74922-z" @default.
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